import utils caching = None restart_from_save = None rng = subconfig().rng patch_size = subconfig().patch_size train_transformation_params = subconfig().train_transformation_params valid_transformation_params = subconfig().valid_transformation_params test_transformation_params = subconfig().test_transformation_params batch_size = 8 nbatches_chunk = 2 chunk_size = batch_size * nbatches_chunk train_valid_ids = utils.get_train_valid_split(PKL_TRAIN_DATA_PATH) train_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH, batch_size=chunk_size, transform_params=train_transformation_params, patient_ids=train_valid_ids['train'], labels_path=TRAIN_LABELS_PATH, slice2roi_path='pkl_train_slice2roi.pkl', full_batch=True, random=True, infinite=True, min_slices=5) valid_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH, batch_size=chunk_size, transform_params=valid_transformation_params, patient_ids=train_valid_ids['valid'], labels_path=TRAIN_LABELS_PATH, slice2roi_path='pkl_train_slice2roi.pkl',
'translation_range_x': (-5, 10), 'translation_range_y': (-10, 10), 'shear_range': (0, 0), 'roi_scale_range': (0.95, 1.3), 'zoom_range': (1., 1.), 'do_flip': True, 'sequence_shift': False } data_prep_fun = data.transform_norm_rescale_after batch_size = 32 nbatches_chunk = 16 chunk_size = batch_size * nbatches_chunk train_valid_ids = utils.get_train_valid_split(PKL_TRAIN_DATA_PATH) train_data_iterator = data_iterators.SliceNormRescaleDataGenerator(data_path=PKL_TRAIN_DATA_PATH, batch_size=chunk_size, transform_params=train_transformation_params, patient_ids=train_valid_ids['train'], labels_path=TRAIN_LABELS_PATH, slice2roi_path='pkl_train_slice2roi_10.pkl', full_batch=True, random=True, infinite=True, data_prep_fun=data_prep_fun) valid_data_iterator = data_iterators.SliceNormRescaleDataGenerator(data_path=PKL_TRAIN_DATA_PATH, batch_size=chunk_size, transform_params=valid_transformation_params, patient_ids=train_valid_ids['valid'], labels_path=TRAIN_LABELS_PATH,